Surgical Stress Promotes the Development of Cancer Metastases by a Coagulation-Dependent Mechanism Involving Natural Killer Cells in a Murine Model
Bibliographic record
Abstract
OBJECTIVE: To determine whether the postoperative hypercoagulable state is responsible for the increase in metastases observed after surgery. BACKGROUND: Surgery precipitates a hypercoagulable state and increases the formation of cancer metastases in animal models. Coagulation promotes metastases by facilitating the formation of microthrombi around tumor cell emboli (TCE), thereby inhibiting natural killer (NK) cell-mediated destruction. METHODS: Mice underwent surgery preceded by tumor cell inoculation to establish pulmonary metastases in the presence or absence of various perioperative anticoagulants. Pulmonary TCE were quantified and characterized using fluorescently labeled fibrinogen and platelets. The role of NK cells was evaluated by repeating these experiments after antibody depletion in a genetically deficient strain and by adoptively transferring NK cells into NK-deficient mice. RESULTS: Surgery resulted in a consistent and significant increase in metastases while a number of different anticoagulants and platelet depletion attenuated this effect. Impaired clearance of TCE from the lungs associated with an increase in peritumoral fibrin and platelet clot formation was observed in surgically stressed mice, but not in control mice or mice that received perioperative anticoagulation. The increase in TCE survival conferred by surgery and inhibited by perioperative anticoagulation was eliminated by the immunological or genetic depletion of NK cells. Adoptive transfer experiment confirms that surgery impairs NK cell function. CONCLUSIONS: Surgery promotes the formation of fibrin and platelet clots around TCE, thereby impairing NK cell-mediated tumor cell clearance, whereas perioperative anticoagulation attenuates this effect. Therapeutic interventions aimed at reducing peritumoral clot formation and enhancing NK cell function in the perioperative period will have important clinical implications in attenuating metastatic disease after cancer surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".